Health insurance plans pay out billions of dollars a year in benefits on behalf of insurance plan participants. Only a small portion of this expenditure, however, is directed to the use of lower cost preventive care to reduce potentially higher cost reactive care. In contrast to reactive health care, preventive health care identifies and reduces the causes of injury and/or illness. A preventive health care regimen, which may include screening for diseases and risk factors, physical examinations, vaccinations, and preventing complications of chronic diseases, may be implemented by health care providers. Health care providers, such as doctors, nurses and their assistants, choose tests, prescribe medicine, make referrals to specialists, counsel and/or use other techniques of proven utility in order to assist the treatment and/or recovery of an individual. Likewise, non-medical case managers can suggest alternatives, such as generic alternatives to name brand drugs, that do not require the advice of a medical professional. Case managers may also provide information to a participant who is unaware of alternative treatments.
Despite the well-documented and obvious benefits of preventive medicine, i.e. reducing unnecessary costs and improving the health of the plan participants, insurance companies that have implemented preventive plans have enjoyed only limited success. This was caused, at least in part, by the inability of case managers and health care providers to accurately identify the candidates within an insurance plan who would most benefit from intervention. In addition, prior art approaches failed to make relevant and useful information available to case managers and health care providers in an efficient and user-friendly manner.
Since the late 1960s, the health insurance industry has performed risk assessment on its insured and potential insureds, particularly for individual major medical insurance. Conventional risk assessment involves evaluating blood tests, analyzing attending physician statements, asking a series of medical history questions, and then applying established guidelines that determine whether a person is 25 percent higher cost risk, 50 percent higher cost risk, etc.
Risk assessment attempts to compensate for deficiencies in experience ratings by assigning risk levels to individuals or a group of enrollees. These risk levels are then used to project the expected costs of subgroups in a population. Existing risk assessment models use two types of data as expected cost predictors: demographic variables and health status. Demographic variables may include age, sex, family status, location, and welfare status, while health status measures can range from self-reported health assessments to requests for diagnoses and prior utilization of medical resources, such as hospitalizations. Models incorporating health status also usually include demographic variables as predictors of costs.
Actuaries have used risk assessment for years in the pricing of health insurance using techniques such as age/sex rating, experience rating, and tier rating. Tier rating is essentially a simplified version of experience rating generally applied to small group populations. Rather than each group having a unique rate based on experience, the experience is used to place that group into one of several “tiers,” the higher-cost tiers reflecting higher historical claims and thus expected costs. HMO premiums for Medicare beneficiaries have also been risk adjusted for more than a decade using variables such as age, sex, geography, welfare and institutional status in a process known as the Adjusted Average Per Capita Cost (“AAPCC”). In more recent years, alternative risk assessment methods have been researched and developed, including models based on health status, as measured by utilization of medical resources and patient diagnoses. The federal government is currently exploring the use of health status measures as alternatives to the AAPCC. Under the umbrella of health care reform, several states have either begun risk adjustment or are in the process of implementing risk adjustment legislation. Risk adjustment refers to the transfer of funds from one plan to another, based upon the risk profile that is observed through risk assessment of all the plans, in an attempt to equalize the playing field among all plans and minimize incentive for avoidance of high-risk enrollees.
Other risk assessment methods include Ambulatory Care Groups (“ACGs”), Diagnostic Cost Groups (“DCGs”), Payment Amounts for Capitated Systems (“PACS”), self-reported health status measures, physiologic health measures, mortality patterns, prior use, the Robinson-Luft Multi-Equation Model, the New York State retrospective conditions/procedures payment method, and an elaborate method using marker diagnoses developed in California.
Risk assessment can be performed prospectively or retrospectively, and the risk adjustment process can also be performed prospectively or retrospectively. Generally, prospective risk assessment uses the experience of one year, such as 2001, to predict the risk attributes of an upcoming year, such as 2002. Prospective risk adjustment occurs when funds are transferred from insurers having relatively high risk profiles, as measured through prospective risk assessment, to those having relatively low (prospective) risk profiles. Prospective risk assessment is also applied in setting capitation rates for provider payment purposes Generally, each insurer builds the expected risk adjustment transfer amounts into their premium rates. A true prospective methodology implies that once the prospective assessments are used to determine transfers, there will be no ultimate transfer of funds based upon actual results. Thus, a true prospective methodology leaves intact a strong incentive to manage medical costs effectively, an incentive that might be removed by retrospective assessment as described below.
Retrospective risk assessment uses the experience of one year to determine the risk assessment attributes of that same year. Likewise, retrospective risk adjustment for a year implies the transfer of payments between carriers based on actual health care costs and risk assessed for that year. A retrospective settlement is an example of retrospective risk adjustment. A reinsurance system for large amount claims is another example of retrospective risk adjustment.
In summary, previous applications of risk assessment and risk adjustment have involved a range of approaches. Efforts by states have typically employed demographic factors such as age, gender, family size and geography, with some method of reinsurance or retrospective adjustment for high cost cases. The application of risk assessment methods in setting capitation payments, profiling providers and performing research on outcomes measurements has typically focused on using age and sex and in some cases, using diagnosis-based approaches such as ACGs and DCGs.